multi-factors rational design strategy for enhancing the thermostability of Escherichia coli AppA phytase

Despite recent advances in our understanding of the importance of protein surface properties for protein thermostability,there are seldom studies on multi-factors rational design strategy, so a more scientific, simple and effective rational strategy is urgent for protein engineering. Here, we first...

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Veröffentlicht in:Journal of industrial microbiology & biotechnology 2013-05, Vol.40 (5), p.457-464
Hauptverfasser: Fei, Baojin, Xu, Hui, Cao, Yu, Ma, Shuhan, Guo, Hongxiu, Song, Tao, Qiao, Dairong, Cao, Yi
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Sprache:eng
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Zusammenfassung:Despite recent advances in our understanding of the importance of protein surface properties for protein thermostability,there are seldom studies on multi-factors rational design strategy, so a more scientific, simple and effective rational strategy is urgent for protein engineering. Here, we first attempted to use a three-factors rational design strategy combining three common structural features, protein flexibility, protein surface, and salt bridges. Escherichia coli AppA phytase was used as a model enzyme to improve its thermostability. Moreover, the structure and enzyme features of the thermostable mutants designed by our strategy were analyzed roundly. For the single mutants, two (Q206E and Y311K), in five exhibited thermostable property with a higher success rate of prediction (40 %). For the multiple mutants, the themostable sites were combined with another site, I427L, we obtained by directed evolution, Q206E/I427L, Y311K/I427L, and Q206E/Y311K/I427L, all exhibited thermostable property. The Y311K/I427L doubled thermostability (61.7 %, and was compared to 30.97 % after being heated at 80 °C for 10 min) and catalytic efficiency (4.46 was compared to 2.37) improved more than the wild-type AppA phytase almost without hampering catalytic activity. These multi-factors of rational design strategy can be applied practically as a thermostabilization strategy instead of the conventional single-factor approach.
ISSN:1367-5435
1476-5535
DOI:10.1007/s10295-013-1260-z